Description Usage Arguments Examples
Plot histograms of observed response frequencies separately for each item (via plot_responses
), and adds the predictions from boeck_predict
.
1 2 3 |
fit_sum |
List. A summary of theta and beta parameters as returned from |
X |
an N x J matrix of observed responses for categories 1...5 (use
|
revItem |
vector of length J specifying reversed items (1=reversed, 0=regular) |
traitItem |
vector of length J specifying the underlying traits (e.g., indexed from 1...5). Standard: only a single trait is measured by all items. If the Big5 are measured, might be something like c(1,1,1,2,2,2,...,5,5,5,5) |
points |
how many resposne categories in Likert scale |
type |
character indicating the type of plotting; actually any of
the |
col |
Color of the prediction line. |
lwd |
The line width, a positive number, defaulting to 2. The interpretation is device-specific, and some devices do not implement line widths less than one. (See the help on the device for details of the interpretation.) |
ylim |
the y limits of the plot. |
measure |
Character vector that indicates whether the mean (default) or the median of the posterior distribution should be plotted. |
... |
Additional arguments passed to |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Not run:
# generate data
N <- 20
J <- 10
betas <- cbind(rnorm(J, .5), rnorm(J, .5), rnorm(J, 1.5), rnorm(J, 0))
dat <- generate_irtree_ext(N = N, J = J, betas = betas, beta_ARS_extreme = .5)
# fit model
res1 <- fit_irtree(dat$X, revItem = dat$revItem, M = 200, warmup = 200)
res2 <- summarize_irtree_fit(res1)
res3 <- tidyup_irtree_fit(res2)
# plot expected and observed frequencies
plot_expected(res3, X = dat$X)
## End(Not run)
|
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